What is Synthetic Data?
Synthetic data is artificially generated data that mimics the statistical properties and structure of real-world data. Instead of collecting data from actual events, synthetic data is created by algorithms, simulations, or generative models to be used for training, testing, and validating AI systems.
Why It Matters
Real-world data is often scarce, expensive, biased, or privacy-restricted. Synthetic data solves all four problems: it can be generated in unlimited quantities, at low cost, with controlled distributions, and without exposing personal information. It's increasingly critical for training autonomous vehicles (simulated driving scenarios), medical AI (synthetic patient records), and for augmenting underrepresented classes in training datasets.
How It Works
Synthetic data generation approaches:
1. Rule-based generation:
- Define statistical distributions and business rules
- Generate data points that match those distributions
- Simple but limited in capturing real-world complexity
2. Generative model-based:
- Train a GAN, VAE, or diffusion model on real data
- The generator produces new samples that follow the learned distribution
- Can produce realistic tabular data, images, text, and time series
3. Simulation-based: